pszemraj/convnextv2-nano-22k-384-boulderspot
Image Classification
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Updated
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38
Datasets:
image
imagewidth (px) 337
338
| label
class label 2
classes | stem
stringlengths 34
70
| suffix
stringclasses 1
value |
|---|---|---|---|
1other
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1other
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1other
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0bouldering_area
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1other
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1other
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1other
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1other
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1other
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1other
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1other
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1other
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1other
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1other
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1other
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1other
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1other
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1other
| IMG_rand_296_0_ll_swiss_image_dop_1020182577123101205 | .png |
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0bouldering_area
| IMG_llswissimagedop1020192762113101205_tile_8960-5120 | .png |
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1other
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1other
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1other
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1other
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1other
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1other
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1other
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1other
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1other
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1other
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1other
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1other
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1other
| imgcode_10202025641190-1050_5400_1200_5550 | .png |
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1other
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1other
| imgcode_10202026641139-7350_5250_7500_5400 | .png |
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1other
| IMG_rand_138_0_ll_swiss_image_dop_1020182667120201205 | .png |
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1other
| imgcode_10201827081142-4950_11400_5100_11550 | .png |
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1other
| imgcode_10202025141169-5550_4800_5700_4950 | .png |
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1other
| IMG_rand_285_0_ll_swiss_image_dop_1020192732123801205 | .png |
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1other
| IMG_rand_281_0_ll_swiss_image_dop_1020182585119901205 | .png |
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1other
| imgcode_10201926811248-4800_6000_4950_6150 | .png |
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1other
| imgcode_10201927611180-600_1500_750_1650 | .png |
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1other
| imgcode_10201826571259-7800_5100_7950_5250 | .png |
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1other
| imgcode_10201827231104-6750_5100_6900_5250 | .png |
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1other
| imgcode_10201927351168-4500_5400_4650_5550 | .png |
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1other
| imgcode_10201826811155-4950_2850_5100_3000 | .png |
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1other
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1other
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1other
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1other
| IMG_rand_78_0_ll_swiss_image_dop_1020182652117701205 | .png |
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1other
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1other
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1other
| IMG_rand_33_0_ll_swiss_image_dop_1020172605113101205 | .png |
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1other
| imgcode_10202025591119-2400_5850_2550_6000 | .png |
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1other
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1other
| imgcode_10201827081142-7200_5700_7350_5850 | .png |
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1other
| imgcode_10201826851157-750_6000_900_6150 | .png |
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1other
| imgcode_10201826021138-1950_4050_2100_4200 | .png |
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1other
| imgcode_10202025631193-7350_8100_7500_8250 | .png |
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1other
| imgcode_10202026081107-1350_10050_1500_10200 | .png |
|
0bouldering_area
| IMG_llswissimagedop1020172608113501205_tile_7424-4352 | .png |
|
1other
| imgcode_10201826561261-150_7200_300_7350 | .png |
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1other
| imgcode_10201726291136-1800_4500_1950_4650 | .png |
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1other
| imgcode_10202025291187-1500_1500_1650_1650 | .png |
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1other
| imgcode_10202026321123-2400_1200_2550_1350 | .png |
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1other
| imgcode_10202024961115-4050_9450_4200_9600 | .png |
|
0bouldering_area
| IMG_POS_magicwood_4_ll_swiss_image_dop_1020192753115801205 | .png |
|
1other
| IMG_rand_714_0_ll_swiss_image_dop_1020192724114901205 | .png |
|
1other
| imgcode_10201926861211-150_4500_300_4650 | .png |
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1other
| IMG_rand_753_0_ll_swiss_image_dop_1020172586112101205 | .png |
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1other
| imgcode_10201927351160-6150_8850_6300_9000 | .png |
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1other
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1other
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1other
| IMG_rand_595_0_ll_swiss_image_dop_1020182716112801205 | .png |
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1other
| IMG_POS_valcalanca_103_ll_swiss_image_dop_1020192732112401205 | .png |
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1other
| imgcode_10201927291124-4500_8700_4650_8850 | .png |
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1other
| imgcode_10201927721138-1350_6000_1500_6150 | .png |
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1other
| imgcode_10201826601220-7350_7200_7500_7350 | .png |
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1other
| imgcode_10201826741179-1200_3150_1350_3300 | .png |
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1other
| imgcode_10201927081178-2400_750_2550_900 | .png |
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1other
| imgcode_10201926911284-150_150_300_300 | .png |
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1other
| imgcode_10201826341206-5550_3750_5700_3900 | .png |
End of preview. Expand
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pszemraj/boulderspot
These are aerial images of Switzerland classified into what could be a bouldering area (label: bouldering_area) or not (label: other). The test set has no labels (i.e. the column is None) and is randomly sampled from across the country.
Sources:
- data: SWISSIMAGE 10 cm
- labels: me
Date created: 2021
You can find some example CNN-based models trained on an earlier/smaller version of this dataset in this repo
If you are a member of an organization interested in details of how this was created/similar ideation related to AI applications for the outdoors/climbing, feel free to contact me (info on my site).
dataset_info:
features:
- name: image
dtype: image
- name: label
dtype: string
- name: stem
dtype: string
- name: suffix
dtype: string
splits:
- name: train
num_bytes: 2881884752.62
num_examples: 13679
- name: test
num_bytes: 871464803.08
num_examples: 3880
download_size: 3137018901
dataset_size: 3753349555.7
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Size of downloaded dataset files:
3.14 GB
Size of the auto-converted Parquet files:
3.14 GB
Number of rows:
17,559